Surface energy balance in the ACCESS models : comparisons with observation based flux products

CSIRO and the Bureau of Meteorology advise that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO and the Bureau of Meteorology (including each of its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. CAWCR Research Letters is an internal serial online publication aimed at communication of research carried out by CAWCR staff and their colleagues. It follows on from its predecessor BMRC Research Letters. Articles in CAWCR Research Letters are peer reviewed and typically 4-8 pages in length. For more information visit the CAWCR website. Introduction An accurate representation of near-surface winds in global forecast models is important in the calculation of surface energy exchange, air pollution dispersion as well as in aviation and wind engineering applications. In a summary paper, Holtslag (2006) points out that a number of model errors can arise from shortcomings in the representation of the stably stratified planetary boundary layer (SBL). A recent intercomparison of Single Column Models (SCMs), which formed part of the second GEWEX Atmospheric Boundary Layer Study found a large spread of results for all model forecast parameters (Svensson and Holtslag, 2007). The greatest difference between the model simulations and observations was in the representation of the diurnal cycle of 10-metre wind speed. The amplitude of this diurnal variation was found to be significantly underpredicted with wind speeds generally too high under nighttime stable conditions. In this paper, model sensitivities to changes in the flux-profile relationships of momentum under stably stratified conditions are investigated using a SCM version of the UK Met Office Unified Model (UM, version 6.3) that forms the atmospheric component of the Australian Community Climate and Earth System Simulator (ACCESS). Flux-profile relationships in the surface layer are commonly described by Monin-Obukhov Similarity Theory (MOST) that expresses the normalised mean gradients as a function of the stability parameter ζ (= z/L, where z is the …

[1]  I. Smith,et al.  Refining rainfall projections for the Murray Darling Basin of south-east Australia—the effect of sampling model results based on performance , 2010 .

[2]  G. Ren,et al.  Changes in means and extreme events of temperature and precipitation in the Asia‐Pacific Network region, 1955–2007 , 2009 .

[3]  O. Alves,et al.  Dynamical Forecast of Inter-El Niño Variations of Tropical SST and Australian Spring Rainfall , 2009 .

[4]  Harry H. Hendon,et al.  Prospects for predicting two flavors of El Niño , 2009 .

[5]  Stephen G. Yeager,et al.  The global climatology of an interannually varying air–sea flux data set , 2009 .

[6]  Thomas R. H. Holmes,et al.  An evaluation of AMSR–E derived soil moisture over Australia , 2009 .

[7]  Cyril J. Morcrette,et al.  PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description , 2008 .

[8]  Peter J. Gleckler,et al.  Improved estimates of upper-ocean warming and multi-decadal sea-level rise , 2008, Nature.

[9]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[10]  J. Arblaster,et al.  Assessing trends in observed and modelled climate extremes over Australia in relation to future projections , 2007 .

[11]  Elizabeth E. Ebert,et al.  Performance of Hourly Operational Consensus Forecasts (OCFs) in the Australian Region , 2007 .

[12]  Swadhin K. Behera,et al.  El Niño Modoki and its possible teleconnection , 2007 .

[13]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[14]  A. Pitman,et al.  Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions , 2007 .

[15]  H. Hendon,et al.  Sensitivity of Australian Rainfall to Inter-El Niño Variations , 2007 .

[16]  G. Hegerl,et al.  Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations , 2007 .

[17]  T. Yamagata,et al.  Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer , 2007 .

[18]  Matthias Drusch,et al.  Initializing numerical weather prediction models with satellite‐derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set , 2007 .

[19]  M. Hoerling,et al.  Unraveling the Mystery of Indian Monsoon Failure During El Niño , 2006, Science.

[20]  Michel Crucifix,et al.  The new hadley centre climate model (HadGEM1) : Evaluation of coupled simulations , 2006 .

[21]  Harry H. Hendon,et al.  Indian Ocean Variability and Its Association with ENSO in a Global Coupled Model , 2005 .

[22]  Peter J. Gleckler,et al.  Surface energy balance errors in AGCMs: Implications for ocean‐atmosphere model coupling , 2005 .

[23]  Pedro Viterbo,et al.  Land-surface, boundary layer, and cloud-field coupling over the southwestern Amazon in ERA-40 , 2005 .

[24]  A. Staniforth,et al.  A new dynamical core for the Met Office's global and regional modelling of the atmosphere , 2005 .

[25]  Randal D. Koster,et al.  Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model , 2005 .

[26]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[27]  Randal D. Koster,et al.  Bias reduction in short records of satellite soil moisture , 2004 .

[28]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[29]  Thomas M. Smith,et al.  An Improved In Situ and Satellite SST Analysis for Climate , 2002 .

[30]  K. Trenberth,et al.  Estimates of Meridional Atmosphere and Ocean Heat Transports , 2001 .

[31]  Kevin E. Trenberth,et al.  Indices of El Niño Evolution , 2001 .

[32]  W. Drosdowsky,et al.  NOTES AND CORRESPONDENCE: Near-Global Sea Surface Temperature Anomalies as Predictors of Australian Seasonal Rainfall. , 2001 .

[33]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[34]  Jean-François Mahfouf,et al.  Evaluation of the Optimum Interpolation and Nudging Techniques for Soil Moisture Analysis Using FIFE Data , 2000 .

[35]  S. Levitus,et al.  Warming of the World Ocean , 2000 .

[36]  L. Rikus,et al.  The operational Australian ultraviolet index forecast 1997 , 1999 .

[37]  J. Hack.  Analysis of the Improvement in Implied Meridional Ocean Energy Transport as Simulated by the NCAR CCM3 , 1998 .

[38]  Kevin E. Trenberth,et al.  The Definition of El Niño. , 1997 .

[39]  Min Zhong,et al.  El Niño, La Niña, and the Nonlinearity of Their Teleconnections , 1997 .

[40]  Fabio Castelli,et al.  Mutual interaction of soil moisture state and atmospheric processes , 1996 .

[41]  D. Randall,et al.  Cloud-radiative effects on implied oceanic energy transports as simulated by Atmospheric General Circulation Models , 1995 .

[42]  B. Barkstrom,et al.  Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment , 1989, Science.

[43]  Jane Hsiung,et al.  Estimates of Global Oceanic Meridional Heat Transport , 1985 .

[44]  N. Nicholls,et al.  Seasonal Relationships between Australian Rainfall and the Southern Oscillation , 1983 .

[45]  Van Genuchten,et al.  A closed-form equation for predicting the hydraulic conductivity of unsaturated soils , 1980 .

[46]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[47]  G. Dobson.  Exploring the Atmosphere , 1963 .

[48]  J. Forrest.  Exploring the Atmosphere , 1963 .

[49]  R. J. Reed,et al.  THE ROLE OF VERTICAL MOTIONS IN OZONE-WEATHER RELATIONSHIPS , 1950 .

[50]  G. Dobson,et al.  Measurements of the amount of Ozone in the Earth's Atmosphere and its Relations to Other Geophysical Conditions. Part 21 , 1926 .

[51]  Australia..  Australian meteorological magazine , 2008 .

[52]  K. Hennessy,et al.  Trends in rainfall indices for six Australian regions: 1910-2005 , 2007 .

[53]  N. Nicholls,et al.  Trends in Australia's climate means and extremes: a global context , 2007 .

[54]  A. Lorenc,et al.  The Met Office global four‐dimensional variational data assimilation scheme , 2007 .

[55]  P. Whetton,et al.  Australian climate change projections derived from simulations performed for the IPCC 4th Assessment Report , 2007 .

[56]  L. Rikus,et al.  UV Index and UV dose distributions for Australia (1997-2001) , 2004 .

[57]  P. Gies,et al.  Global Solar UV Index: Australian Measurements, Forecasts and Comparison with the UK¶ , 2004, Photochemistry and photobiology.

[58]  J. S. Godfrey,et al.  A New Version of the Australian Community Ocean Model for Seasonal Climate Prediction , 2002 .

[59]  R. Smith.  A scheme for predicting layer clouds and their water content in a general circulation model , 1990 .

[60]  T. H. Haar,et al.  Estimating the Meridional Energy Transports in the Atmosphere and Ocean , 1985 .